Imperial College London

MrDigbyChappell

Faculty of EngineeringDepartment of Computing

Research Postgraduate
 
 
 
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Contact

 

d.chappell19 Website CV

 
 
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Location

 

Dyson BuildingSouth Kensington Campus

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Summary

 

Summary

Digby Chappell is a PhD Student (Research Postgraduate) in the UKRI CDT in Artificial Intelligence for Healthcare at Imperial College London. He is part of three labs:REDS Lab, the Robot Intelligence Lab, and the SiMMS Research Group, as part of a cross-disciplinary PhD project that combines robotics, machine learning, and medical research. Digby's research focuses on improving Prosthetic Hand Control from a holistic point of view, considering methods to improve user training, provide sensory feedback, and investigating novel hand designs, as well as developing next generation myoelectric control algorithms.

Prior to joining Imperial, Digby completed an MEng at the University of Cambridge, with his final year project focusing on "Wearable Muscle Activity Sensors" in the Cambridge Bioelectronics Laboratory.

More information about Digby can be found at the following links:

LinkedIn

Scholar

GitHub

Publications

Journals

Chappell D, Son HW, Clark AB, et al., 2022, Virtual reality pre-prosthetic hand training with physics simulation and robotic force interaction, Ieee Robotics and Automation Letters, Vol:7, ISSN:2377-3766, Pages:1-1

AlAttar A, Chappell D, Kormushev P, 2022, Kinematic-model-free predictive control for robotic manipulator target reaching with obstacle avoidance, Frontiers in Robotics and Ai, Vol:9, ISSN:2296-9144, Pages:1-9

Conference

Berkovic A, Laganier C, Chappell D, et al., A multi-modal haptic armband for finger-level sensory feedback from a prosthetic hand, EuroHaptics, Springer

Yang Z, Clark A, Chappell D, et al., Instinctive real-time sEMG-based control of prosthetic hand with reduced data acquisition and embedded deep learning training, IEEE International Conference on Robotics and Automation

Cursi F, Chappell D, Kormushev P, 2022, Augmenting loss functions of feedforward neural networks with differential relationships for robot kinematic modelling, 20th International Conference on Advanced Robotics (ICAR), IEEE, Ljubljana, Slovenia, Pages:201-207

More Publications